class_docstr: Similar to ``sklearn.feature_selection.RFECV`` but with distributed
  feature set removal and cross validation. Fits the base estimator on
  the input training data once for each left out feature sets according
  to the `step` and the scoring of the features according to an initial
  estimator fit with all features. Uses cross validation to score each
  feature set. Results in the fitted base estimator with the best feature set.
  
  Roughly approximates `RFECV` particularly as `step` approaches 1 and
  `min_features_to_select` approaches 0. The value-add is in the
  embarrassingly parallel nature of the `DistFeatureEliminator` algorithm.
  
  Args:
      estimator (estimator object):
          This is assumed to implement the scikit-learn estimator interface.
          Either estimator needs to provide a ``score`` function,
          or ``scoring`` must be passed.
      sc (sparkContext): Spark context for spark broadcasting and rdd operations.
      partitions (int or 'auto'): default 'auto'
          Number of partitions to use for parallelization of parameter
          search space. Integer values or None will be used directly for `numSlices`,
          while 'auto' will set `numSlices` to the number required fits.
      min_features_to_select (int): Minimum number of features to keep after elimination.
      step (int or float): If greater than or equal to 1, then `step`
          corresponds to the (integer) number of features to remove at each iteration.
          If within (0.0, 1.0), then `step` corresponds to the percentage
          (rounded down) of features to remove at each iteration.
      cv (int or cv object): Determines the cross-validation splitting strategy.
      scoring (str or callable): Scoring function or callable to evaluate
          predictions on the training set for feature elimination.
      verbose (int or bool): Controls the verbosity: the higher, the more messages.
      n_jobs (int): Number of jobs to run in parallel if using joblib.
      pre_dispatch (bool): Controls number of jobs dispatched if using joblib.
      mask (bool): Whether or not to apply feature selection mask to input data
          before making predictions.
code: def transform(self, X):
      self._check_is_fitted()
      return self.best_estimator_.transform(self._apply_mask(X))
  
imports: import numpy
  from sklearn.base import BaseEstimator, ClassifierMixin, is_classifier
  from sklearn.utils.metaestimators import if_delegate_has_method
  from sklearn.utils import check_X_y, safe_sqr
  from sklearn.model_selection import check_cv
  from sklearn.metrics import check_scoring
  from joblib import Parallel, delayed
  from itertools import product
  from scipy.sparse import issparse
  from .validation import _check_is_fitted
  from .base import _parse_partitions, _clone
  from .utils import _safe_split
label_desc: Use image node_id 9 for calling the DistFeatureEliminator obj's underlying member method code with example usage: obj.transform(X) and returns: self